Conventional multi-lead electrocardiogram (ECG) systems capture cardiac signals from a fixed set of anatomical viewpoints defined by lead placement. However, certain cardiac conditions (e.g., Brugada syndrome) require additional, non-standard viewpoints to reveal diagnostically critical patterns that may be absent in standard leads. To systematically overcome this limitation, Nef-Net was recently introduced to reconstruct a continuous electrocardiac field, enabling virtual observation of ECG signals from arbitrary views (termed Electrocardio Panorama). Despite its promise, Nef-Net operates under idealized assumptions and faces in-the-wild challenges, such as long-duration ECG modeling, robustness to device-specific signal artifacts, and suboptimal lead placement calibration. This paper presents NEF-NET+, an enhanced framework for realistic panoramic ECG synthesis that supports arbitrary-length signal synthesis from any desired view, generalizes across ECG devices, and compensates for operator-induced deviations in electrode placement. These capabilities are enabled by a newly designed model architecture that performs direct view transformation, incorporating a workflow comprising offline pretraining, device calibration tuning steps as well as an on-the-fly calibration step for patient-specific adaptation. To rigorously evaluate panoramic ECG synthesis, we construct a new Electrocardio Panorama benchmark, called Panobench, comprising 5367 recordings with 48-view per subject, capturing the full spatial variability of cardiac electrical activity. Experimental results show that NEF-NET+ delivers substantial improvements over Nef-Net, yielding an increase of around 6 dB in PSNR in real-world setting. The code and Panobench will be released in a subsequent publication.

Think back through your day and consider all the amazing tasks your brain has helped you perform. From brushing your teeth to eating your lunch and reading the words on this page, your thoughts, feelings and actions may appear to be the product of a finely tuned machine. Simply telling someone your name is a small miracle for electrical signals zapping across a 1.3-kilogram lump of jelly. "You're pulling off one of the most complicated and exquisite acts of computation in the universe," says Keith Hengen, a biologist at Washington University in St Louis. Exactly how we achieve this complexity has puzzled philosophers and neuroscientists for centuries, and now it seems precision isn't the answer. Instead it could all come down to the brain's inherent messiness.

Breakthroughs, discoveries, and DIY tips sent every weekday. A US astronaut aboard the International Space Station (ISS) recently caught a glimpse of one of Earth's least understood atmospheric phenomena. While orbiting in the early hours of July 3, Nichole "Vapor" Ayers snapped a photo of a transient luminous event, as she passed over North America. Better known as a sprite, these atmospheric events are common after a lightning strike. Wow," Ayers posted to social media later that day along with the stunning picture.

But if you regularly turn to ChatGPT, a new study may raise alarm bells. Scientists from MIT Media Lab have warned that using AI could impact your ability to learn, think and remember. In their study, the team measured electrical activity in the brain to track 54 students over several essay-writing sessions. One group used ChatGPT, another used Google, and the last had no external help at all. The results revealed that students who used large language models (LLM) like ChatGPT to write essays showed poorer memory, reduced brain activity and weaker engagement than those who used other methods.

This study introduces a deep learning model based on the U-net architecture to reconstruct missing leads in electrocardiograms (ECGs). Using publicly available datasets, the model was trained to regenerate 12-lead ECG data from reduced lead configurations, demonstrating high accuracy in lead reconstruction. The results highlight the ability of the model to quantify the information content of each ECG lead and their inter-lead correlations. This has significant implications for optimizing lead selection in diagnostic scenarios, particularly in settings where full 12-lead ECGs are impractical. Additionally, the study provides insights into the physiological underpinnings of ECG signals and their propagation. The findings pave the way for advancements in telemedicine, portable ECG devices, and personalized cardiac diagnostics by reducing redundancy and enhancing signal interpretation.

Tattoos printed onto a person's scalp can detect electrical activity in the brain and carry signals to a recording device Analysing brainwaves could be made easier by printing a temporary tattoo onto a person's head. Electroencephalography (EEG) is a way of measuring electrical activity in the brain via electrodes placed on the scalp. It can be used to test patients for neurological conditions such as epilepsy, tumours or injury from stroke or traumatic impacts to the head. Because people's skulls vary in size and shape, technicians have to spend considerable amounts of time measuring and marking the scalp to get accurate readings. A gel helps the electrodes detect brain signals, but it stops working well as it dries.

From breaking down toxins to changing the inner workings of the human mind, mushrooms are capable of some seriously impressive features. But now, researchers have taken a fungi's amazing abilities to a new level as they teach a mushroom to crawl in a robot body. Scientists from Cornwell University in New York have created a new type of'biohybrid robot' which puts the humble mushroom in the driver's seat. Natural electrical signals in the mushroom that are triggered by light are able to control the hybrid device's insect-style legs. The researchers say that robots of the future could make use of these fungal brains to respond to navigate more unpredictable environments.

Physiological fatigue, a state of reduced cognitive and physical performance resulting from prolonged mental or physical exertion, poses significant challenges in various domains, including healthcare, aviation, transportation, and industrial sectors. As the understanding of fatigue's impact on human performance grows, there is a growing interest in developing effective fatigue monitoring techniques. Among these techniques, electroencephalography (EEG) has emerged as a promising tool for objectively assessing physiological fatigue due to its non-invasiveness, high temporal resolution, and sensitivity to neural activity. This paper aims to provide a comprehensive analysis of the current state of the use of EEG for monitoring physiological fatigue. Keywords: EEG, fatigue, physical activity, brain-computer interface, wearable device, healthcare 1. Introduction Since 1878 the French physiologist Angelo Mosso [52] has carried out pioneering studies of the blood circulation in the brain during mental and physical work, initiating an understanding of the physiological basis of fatigue and the study of physiological fatigue, research efforts have already spanned several disciplines, including psychology, physiology, neurology, and occupational health. Over the years, scientists and researchers have made significant contributions to understanding the nature, causes, and consequences of physiological fatigue. The prediction of physiological fatigue is critical in areas where performance, safety, human well-being and especially sports are of paramount importance. By understanding and predicting fatigue levels it is possibly take proactive steps to reduce fatigue-related risks, optimize performance, and improve overall health and safety.

Fashionable earbuds can be used to detect an irregular heartbeat, by tracking the electrical activity of the heart. Researchers at Imperial College London have created a device worn inside the ear, and just a little larger than an earbud, which can take an ECG reading all day long. A new study has found it performs about as well as a conventional ECG using electrodes on the chest, for two out of three measurements. But while the earbud detects a weaker signal from the heart than a chest ECG, because it is further away across the body, it has the advantage of being worn easily for hours at a time. That could help people detect much more quickly, without visiting a doctor, if they have a heart rhythm disorder.

Listening to an audiobook before bed affects a person's brain activity after they nod off and the content of their dreams. Better understanding this could lead to therapies that help to treat certain mental health conditions by targeting memory processing during sleep. While a person sleeps, their brain spontaneously "replays", or reactivates, patterns of electrical activity that are related to learning, to transfer important new information to long-term memory storage.